Abstract

To explore the determinants of hepatic uptake, 16 compounds were investigated with different physicochemical and disposition characteristics, including five statins, three sartans, saquinavir, ritonavir, erythromycin, clarithromycin, nateglinide, repaglinide, fexofenadine, and bosentan. Freshly isolated rat hepatocytes in suspension were used with the oil-spin method to generate kinetic parameters. Clearances, via passive diffusion (Pdiff) and active uptake (CLactive, characterized by maximal uptake rate and Km), were estimated from the initial uptake rate data over a 0.01 to 100 μM concentration range. The Km values had a range of 15-fold, with 10 of the 16 drugs with Km < 10 μM (median 6 μM). Both CLactive and Pdiff ranged over 100-fold (median 188 and 14 μl/min/106 cells). Assessment of the relative contribution of Pdiff and CLactive indicated that, at low concentrations (approximately 0.1 μM), the active process contributes >80% to the overall uptake for 13 drugs. Although high Pdiff values were obtained for ritonavir and repaglinide, active process contributed predominantly to uptake; in contrast, high passive permeability dominates over transporter-mediated uptake for saquinavir over the full concentration range. For bosentan and erythromycin, active and passive processes were equally important. Hepatocyte-to-medium unbound concentration ratio was >10 for 9 of the 16 drugs, ranging from 2 to 494 for bosentan and atorvastatin, respectively. Some drugs showed extensive intracellular binding (fraction unbound range 0.01–0.6), which was not correlated with active uptake. LogD7.4 correlated significantly with Pdiff and the extent of intracellular binding but not with active uptake. This study provides systematic assessment of the role of active uptake relative to the passive process; implications of the findings are discussed.

Despite the increasing number of in vitro transporter studies, a lack of consistency in rat and human in vitro uptake methodology and data is apparent, particularly in the assessment and modeling of passive permeation, drug efflux, and nonspecific binding (Kitamura et al., 2008; Paine et al., 2008; Poirier et al., 2008; Watanabe et al., 2009a). In many cases, uptake is limited to use of a single low- and high-substrate concentration to estimate saturable and nonsaturable uptake, respectively. In addition, efforts have focused primarily on single or a small number of substrates, and hence, there is no comprehensive database of values. This contrasts with the amount of information available on hepatic metabolism where a wide range of clearance values both in vitro (hepatocytes and microsomes) and in vivo are documented (Ito and Houston, 2004; Gertz et al., 2010; Hallifax et al., 2010).

The rat often provides a useful model for characterizing drug disposition before detailed human studies and for mechanistic studies (Watanabe et al., 2009a, 2010). This source of hepatic tissue for in vitro studies has the advantage of being much more reproducible than the human; of particular concern are tissue storage, interdonor variability, and deviations from standard protocols in cell isolation, all of which are minimal when animal tissues are used. Thus, notwithstanding the documented species differences in both drug-metabolizing enzymes and hepatic transporters (Hagenbuch and Gui, 2008), the rat offers many attractions as a model system for humans.

There are a number of approaches to determining hepatic uptake in vitro; however, the common principle involves separation of cells and media and the monitoring of drug concentrations in either matrix or both matrices (Petzinger and Fuckel, 1992; Hallifax and Houston, 2006; Poirier et al., 2008). In the present study, drug associated with the hepatocytes was measured after separation from free drug by layering the suspension of drug and hepatocytes over silicone/mineral oil followed by rapid centrifugation − the oil-spin method (Petzinger and Fuckel, 1992), a method used by several groups (Ishigami et al., 1995; Nakai et al., 2001; Nezasa et al., 2003; Shimada et al., 2003; Hallifax and Houston, 2006) to characterize hepatic uptake by a clearance term for active transport, CLactive, together with Km and Vmax and a passive permeability parameter, Pdiff. As enzyme-transporter interplay complicates the interpretation of many of these types of studies, the nonspecific P450 inhibitor, l-aminobenzotriazole (ABT) (Mico et al., 1988), has been used to eliminate any potential metabolism. Intracellular binding has also been determined to complete the characterization of hepatocellular uptake.

The present study focuses on a group of 16 drugs that are likely OATP substrates (Shitara and Sugiyama, 2006; Kalliokoski and Niemi, 2009; Giacomini et al., 2010) and show a range of properties in terms of their physicochemical nature and metabolism (Table 1). These include five HMG-CoA reductase inhibitors (rosuvastatin, atorvastatin, pravastatin, pitavastatin, cerivastatin), three sartans (telmisartan, valsartan, olmesartan), saquinavir, ritonavir, erythromycin, clarithromycin, nateglinide, repaglinide, fexofenadine, and bosentan. For many of the drugs selected, there are clinical data in subjects with polymorphic OATP1B1 to support the contributing role of uptake (Kalliokoski et al., 2008; Ieiri et al., 2009). In addition, many of these drugs are associated with clinical drug-drug interactions that are believed to be, at least partially, mediated by transporters (Kajosaari et al., 2005; Hirano et al., 2006). However, there is a lack of supporting in vitro uptake data to assess the contribution of the active uptake relative to the passive process and intracellular binding of these drugs.

Hepatocyte Studies.

For hepatocyte preparations, anesthetized rats were sacrificed by cervical dislocation, and hepatocytes were prepared using an adaptation of the collagenase perfusion method as described previously (Hayes et al., 1995). Hepatocyte viability was determined using the trypan blue exclusion test, and only those hepatocyte preparations with viabilities greater than 85% were used. All kinetic and inhibition studies were performed in duplicate under initial rate conditions, with respect to incubation time and hepatocyte density. All hepatocyte studies were performed using three to five independent hepatocyte preparations.

Animal Source, Housing, and Diet.

Male Sprague-Dawley rats (240–260 g) were obtained from the Biological Sciences Unit, Medical School, University of Manchester (Manchester, UK). They were housed in groups of two to four, in opaque boxes on a bedding of sawdust in rooms maintained at a temperature of 20 ± 3°C, with a relative humidity of 40 to 70% and a 12-h light/dark cycle. The animals were allowed free access to Chow Rat Mouse diet and fresh drinking water. The University of Manchester review committee approved all animal protocols.

Experimental Design.

To characterize the uptake kinetics and evaluate the contribution of transporter-mediated active uptake to the overall clearance, studies were performed in rat hepatocytes in suspension by the oil-spin method (Hallifax and Houston, 2006) over a 0.01 to 100 μM (n = 8–10) concentration range. Clearance via passive diffusion (Pdiff), maximal uptake rate (Vmax), and Michaelis constant (Km) were estimated from the initial uptake rate data, and the parameters were used for the subsequent analysis of the uptake characteristics of the compounds.

For the initial rate experiments used to determine the above kinetic parameters, three data points obtained over the first 1.5 min were used. In additional experiments, longer incubation times were used (90 min) to allow attainment of equilibrium to obtain the hepatocyte/medium concentration partition coefficient (Kptotal).

For the five statins, saquinavir, ritonavir, clarithromycin, erythromycin, and telmisartan, rat hepatocytes were suspended in Krebs-Henseleit buffer, pH 7.4, at a concentration of 2 × 106 cells/ml and preincubated at 37°C for 5 min. The uptake study was initiated by the addition of equal volume of buffer containing 3H-labeled and unlabeled compounds at substrate concentration range of 0.01 to 100 μM (final cell concentration: 1 × 106 cells/ml). Aliquots were taken and placed in the narrow tube containing silicone-mineral oil (density, 1.015; Sigma-Aldrich) on the top of 3 M potassium hydroxide solution, followed by centrifugation through the silicone-mineral oil layer at the designated time points (30, 60, and 90 s or 20, 60, and 100 s) to separate cells from media. The radioactivity in both cells and media was determined by liquid scintillation counting. For nonradiolabeled drugs, olmesartan, valsartan, repaglinide, nateglinide, fexofenadine, and bosentan, rat hepatocytes were suspended in buffer at a concentration of 4 × 106 cells/ml with an equal volume of buffer containing 4 mM ABT added 15 min before to inhibit potential cytochrome P450-mediated metabolism (Parker and Houston, 2008) followed by preincubation at 37°C for 5 min. The uptake study for nonradiolabeled compounds in the dataset (e.g., repaglinide) was performed as described for radiolabeled compounds, with the exception that 5 M ammonium acetate solution was used as the bottom layer instead of alkaline solution. After separation of cells from media by centrifugation, the tubes were frozen in the liquid nitrogen. After thawing, an aliquot (50 μl) of media was taken and quenched in 100-μl methanol; the tubes were frozen again, and the tube was cut into a 1.5-ml centrifuge tube, including 200 μl of methanol, and bottom layer was and thawed again, the substrate concentration of both media and cellular fraction were analyzed using LC-MS/MS.

LC-MS/MS Analysis.

The LC-MS/MS systems used consisted of either a Waters 2790 with a Micromass Quattro Ultima triple quadruple mass spectrometer (Waters, Milford, MA) or an Agilent 1100 (Agilent Technologies, Santa Clara, CA) with a Micromass Quattro-LC triple quadruple mass spectrometer. Samples were centrifuged at 2500 rpm for 10 min, and an aliquot of 10 μl of both the dialysate and buffer was analyzed by LC-MS/MS. Varying gradients of four mobile phases were used, the compositions of which were 1) 90% water and 0.05% formic acid with 10% acetonitrile, 2) 10% water and 0.05% formic acid with 90% acetonitrile, 3) 90% water and 10 mM ammonium acetate with 10% acetonitrile, and 4) 10% water and 10 mM ammonium acetate with 90% acetonitrile.

For bosentan, repaglinide, valsartan, olmesartan, nateglinide, and fexofenadine, a Luna C18 column (3 μm, 50 × 4.6 mm) was used for chromatographic separation of analytes (Phenomenex, Torrance, CA). The flow rate was set at 1 ml/min, and this was split to 0.25 ml/min before entering the mass spectrometer. Mass transitions were 552.4 > 202.2, 453.3 > 230.2, 436.5 > 235.3, 502.3 > 466.2, 447.4 > 207.3 and 318.15 > 166.15 for bosentan, repaglinide, valsartan, fexofenadine, olmesartan, and nateglinide, respectively. Mibefradil was used as an internal standard for bosentan, valsartan, olmesartan, and nateglinide, whereas indomethacin was applied for the analysis of repaglinide samples and terfenadine for fexofenadine sample analysis.

Data Analysis.

The hepatic uptake clearance (CLuptake) was determined from the slope of the plot of cell-to-media ratio of radioactivity (concentration) versus time and used to calculate initial uptake velocity by multiplying by substrate concentration. Initial uptake velocity (v) is expressed in eq. 1:
where Pdiff is the clearance via passive diffusion and Km and Vmax are the Michaelis constant and the maximal uptake rate for the saturable uptake, respectively. Kinetic parameters were estimated by simultaneous fitting of all time and concentration points using nonlinear regression in WinNonlin (Pharsight, Palo Alto, CA). The CLactive was calculated from the ratio of Vmax over Km, whereas the total uptake clearance (CLuptake) included both the active and passive component (CLactive and Pdiff). In addition, relative importance of the active hepatic uptake in comparison to the passive process was estimated over the range of concentrations investigated.

The parameter Kptotal, which reflects intracellular binding in addition to active uptake processes, was calculated from eq. 2.
where Ccell and CM represent concentration in the cell and media, respectively. The hepatocyte-to-medium partition coefficient for unbound drug concentration (Kpu), which provides a measure of the cytosolic cellular concentration relative to the external medium and, hence, reflects active uptake was calculated from eq. 3.

These two partitioning parameters are related by intracellular binding, as measured by the third partitioning parameter Kpi.
where Kpi can be regarded as the ratio of the total cellular concentration to cytosolic unbound drug concentration (Parker and Houston, 2008) and is reflective on the fraction unbound in the hepatocyte (fucell).

Hence,

The parameter fucell was calculated using eq. 5, with the exception of pravastatin, cerivastatin, and atorvastatin. For these drugs, fucell was obtained using the logD7.4 data and the regression equation logfucell = −0.9161 − 0.2567 logD7.4 based on the remaining drugs (n = 13), as illustrated in Fig. 3B. The logD7.4 for the drugs investigated were determined experimentally and kindly provided by Drug Metabolism and Pharmacokinetics Research Laboratories, R&D Division, Daiichi-Sankyo Co., Ltd. (Tokyo, Japan). Polar surface area for individual drugs was generated from Molinspiration Cheminformatics software (http://www.molinspiration.com) using chemical structure of the drugs and corresponding SMILES (Simplified Molecular Input Line Entry Specification).

Results

Uptake kinetics for 16 drugs was investigated in freshly isolated rat hepatocytes in suspension. A time-dependent increase in cell-to-media concentration ratio was observed for all of the compounds investigated, and the use of a wide concentration range (0.01–100 μM) allowed full characterization of the uptake process (Fig. 1). Nonlinear regression was used to estimate the kinetic parameters for passive (Pdiff) and active (Km, Vmax, and CLactive) processes for the 16 drugs investigated (Table 2).

Kinetic parameters for the hepatic uptake of 16 drugs in rat hepatocytes in suspension

Values represent the mean ± S.D. of three to five experiments.

Clearance by active transport is expressed as a percentage of the total uptake clearance to assess the importance of transporter activity for each of 16 compounds investigated in Table 2. For all of the compounds investigated, hepatic uptake showed >50% dependence on transporters at low (possibly therapeutic) concentrations; for 13 of the 16 drugs investigated, this contribution was >80%. Figure 1 illustrates three distinct types of active uptake in relation to the passive process: 1) rosuvastatin, for which active processes dominate over the full concentration range studied (95% uptake); 2) pitavastatin, for which active uptake is also substantial (88% of uptake), yet readily saturable and, hence, passive permeability shows increasing importance, as drug concentration increases; and 3) saquinavir, for which passive permeability is comparable to active uptake at low concentrations (48% uptake) but dominates at high concentrations.

Pdiff values (Fig. 2A) covered two orders of magnitude ranging from approximately 1 (olmesartan and pravastatin) to over 100 (ritonavir and saquinavir), with a median of 14 μl/min/106 cells (Table 2). It is noteworthy that bosentan, saquinavir, and erythromycin, the three drugs with the lowest percentage contribution (approximately 50%) to uptake from transporters, show relatively high Pdiff (>10 μl/min/106 cells). There was a strong (r2 = 0.867) and statistically significant (p < 0.001) correlation between Pdiff and the logD7.4 value (Fig. 3A).

Hepatocellular uptake characteristics of 16 drugs in rat isolated hepatocytes. Relationship between Pdiff and CLactive (A) and Pdiff and Km (B). The solid line in panel A represents a 12-fold difference from the line of unity which is indicated as a dashed line. Eleven drugs consistent with this trend are shown within the ellipse and outliers identified: atorvastatin (1), rosuvastatin (2), bosentan (3), erythromycin (4), saquinavir (5), and clarithromycin (6).

Role of logD7.4 in defining Pdiff (A) and fucell (B). Relationships between parameters were best described by the following equations: logPdiff = 0.3207 logD7.4 + 0.7000 and logfucell = −0.9161 − 0.2567 logD7.4. The latter equation (based on 13 drugs from the current dataset) was subsequently used to estimate fucell for atorvastatin, cerivastatin, and pravastatin.

Km values for active transport have a median value of 6 μM (see Fig. 2B). The lowest Km values were obtained for ritonavir and repaglinide (approximately 2 μM), in contrast to clarithromycin, erythromycin, saquinavir, and olmesartan where values were greater than 50 μM (Table 2). The parameter Vmax appears more consistent across the 16 compounds showing a median value of 850 pmol/min/106cells; with saquinavir and bosentan at the higher and lower end, respectively (Table 2).

CLactive is defined by both Km and Vmax and, the dominant parameter was drug-dependent for the 16 compounds investigated. The range of CLactive covered two orders of magnitude from 10.6 to 1500 μl/min/106 cells for olmesartan and atorvastatin, respectively, with a median value of 188 μl/min/106 cells (Fig. 2A). There was no statistical relationship between CLactive (or Km) and Pdiff values. However, it was useful to consider these relationships (Fig. 2, A and B), as certain trends can be identified. For 11 drugs, CLactive was approximately 12-fold higher than the Pdiff value (range 5–19). The most pronounced outliers were rosuvastatin and atorvastatin where this ratio was approximately 60 and 280, respectively. In the case of saquinavir, erythromycin, and bosentan, values for CLactive and Pdiff were approximately equivalent. When Km values are considered in relation to Pdiff (Fig. 3B), a clear trend was evident for 13 drugs where Km decreased as Pdiff increased; outliers represented drugs with high Km values, namely clarithromycin, erythromycin, and saquinavir.

Kpu (calculated from CLuptake and Pdiff and shown in Fig. 4A) varied more than 200-fold, ranging from 2 in the cases of erythromycin, saquinavir, and bosentan to 494 for atorvastatin (Table 3). The extremes were the same drugs as discussed above in terms of the relative magnitude of Pdiff and CLactive. For 56% of the drugs in the dataset, the Kpu value was 10 and above. For most drugs, Kptotal was substantially higher than Kpu (10–130-fold), with median values of 232 and 13, respectively (Table 3) but less so for rosuvastatin (2-fold) and valsartan (6-fold). For olmesartan, the two Kp values were approximately equal (see Fig. 4A). However, there was no statistically significant trend between these two parameters. The intracellular binding process, as indicated by fucell, was related to logD7.4 (p < 0.001, r2 = 0.735), as shown in Fig. 3B. Figure 4B supports the notion of the independence between transporter-mediated uptake (as measured by Kpu − median value of 13 indicated in Fig. 4B) and intracellular binding (fucell). Therefore, measurement of only one of these processes will limit the characterization of hepatocellular drug accumulation.

Discussion

The hepatic uptake characteristics of a series of 16 drugs were investigated to provide a dataset of parameters for comparative purposes. Currently, the kinetic information available on both active and passive uptake to provide a framework for evaluating new compounds is limited. To date, studies have primarily focused on single compounds or were not carried out over a sufficiently wide concentration range to achieve the above objectives. In contrast, there are several studies documenting inhibitory properties of many drugs against various hepatic transporters, particularly OATP1B1 (Hirano et al., 2006; Noe et al., 2007; Gui et al., 2009; Sharma et al., 2009).

In the current study, the rat was selected as the source of hepatocytes for several reasons. These rat hepatocytes were freshly isolated to eliminate any concerns associated with tissue storage or interdonor variability, commonly observed with human material, and hence had the advantage of being a more reproducible in vitro system. The rat has also proved to be a useful model for characterizing drug hepatic distribution in vivo where multiple indicator dilution studies have been carried out (Yamazaki et al., 1993; Watanabe et al., 2009a). Furthermore, the use of freshly isolated cells in suspension where internalization of efflux transporters has been documented (Bow et al., 2008) combined with the treatment with ABT (Hallifax and Houston, 2006) to eliminate P450 metabolism has provided a valuable system to focus primarily on uptake characteristics. Despite existing species differences in transporters (Hagenbuch and Gui, 2008), this strategy provides valuable basic information on hepatocellular drug uptake, which can be used for a variety of purposes, including initial optimization of hepatic uptake in physiologically-based pharmacokinetic models (Watanabe et al., 2009a).

Active processes were confirmed as important for the uptake of these 16 drugs at low (therapeutic) concentrations (<0.1 μM); for 13 drugs, this process showed a greater than 80% contribution to total hepatic uptake. For the remaining three drugs—bosentan, erythromycin, and saquinavir—the importance of passive and active uptake was equal. In the case of saquinavir, both Pdiff and CLactive were large, whereas both parameters had low values for bosentan and erythromycin.

Vmax values were more consistent across the compound set (median 850 pmol/min/106 cells) but saquinavir, ritonavir, telmisartan, and clarithromycin showed values >2000 pmol/min/106 cells. Both CLactive and Pdiff values ranged over more than two orders of magnitude. It is of interest that the range of Pdiff (1–200 μl/min/106 cells) and CLactive values (10–1500 μl/min/106 cells) reported in these studies were comparable to that previously documented for metabolic intrinsic clearance (1–1800 μl/min/106 cells) (Ito and Houston, 2004) in freshly isolated rat hepatocytes. The rank order for uptake clearance of pitavastatin, rosuvastatin, pravastatin, valsartan, and olmesartan was in good agreement with previously published values determined at a single low concentration (Watanabe et al., 2009b). Although no statistical relationship between CLactive and Pdiff was established with this set of drugs, the association between these two parameters for specific cases provides a useful framework for discussion. A saturable uptake mechanism was responsible for >88% of the hepatic uptake of pitavastatin, pravastatin, atorvastatin, and rosuvastatin; the statins recommended as candidate probes for clinical transporter-mediated drug-drug interaction studies by the recent transporter consortium “white” paper (Giacomini et al., 2010). Pitavastatin and cerivastatin showed a larger passive clearance (approximately 20 μl/min/106 cells) compared with atorvastatin, pravastatin and rosuvastatin (1–7 μl/min/106 cells); however, the saturable component was characterized by similar low Km values (Table 2). Fexofenadine, valsartan, and olmestartan showed similar trends to the latter three statins, whereas uptake characteristics of telmisartan and nateglinide were comparable to pitavastatin and cerivastatin.

Both saquinavir and ritonavir have large passive clearances of 191 and 118 μl/mim/106 cells, respectively (Table 2), resulting in the large uptake rates at higher concentration. However, considering the low Km value of 2.6 μM for ritonavir uptake, an active process is probably an important contributor to the uptake of ritonavir. The contribution of its active process is dominant over passive process at low substrate concentrations less than 10 μM. In contrast to ritonavir, uptake of saquinavir was characterized by a more pronounced passive uptake contributing >50% at the lower substrate concentration. The Km value for the active uptake (52 μM) was consistent with the previously reported value (Parker and Houston, 2008). Repaglinide uptake kinetics was comparable to ritonavir. The high affinity for uptake transporters seen for repaglinide was in agreement with a number of clinical studies indicating the importance of hepatic uptake for disposition of this drug (Kajosaari et al., 2005; Kalliokoski et al., 2008). For bosentan and erythromycin, active and passive processes were equally important. Clarithromycin, by virtue of its high Vmax, has a much greater dependence on active uptake.

The associations between each of the kinetic parameters and with physicochemical properties highlight our limited understanding of the determinants of hepatic uptake and emphasize the need for further experimentation with larger datasets. Although the positive correlation between Pdiff and logD7.4 was of no surprise, any relationship between Pdiff and CLactive was not prominent. There was a tendency (although not statistically significant) for these two parameters to be positively related (CLactive being approximately 10 times Pdiff), in particular for six drugs with the lowest and highest Pdiff. In contrast, 10 drugs with intermediate Pdiff values (approximately 10 μl/min/106cells) showed CLactive that ranged from 500-fold larger to equal to Pdiff. The relationship between Km and Pdiff was negative for 13 of the 16 drugs. The high Km outliers represented, in the main, the drugs with comparable CLactive and Pdiff parameter values.

Unbound hepatocyte-to-medium concentration ratio (Kpu) and the extent of intracellular binding were indirectly obtained from the parameter estimates defining active and passive uptake and, therefore, reflect any variability or uncertainty in these parameter estimates. However, as discussed earlier, the experimentally manipulated lack of metabolic clearance and efflux transporters in these studies provided Kpu values calculated solely from the clearances for active uptake and passive permeability and, hence, should represent pure distribution parameters (Brown et al., 2010). For nine drugs in the dataset Kpu > 10, whereas smaller (but >2) values were obtained for saquinavir, erythromycin, valsartan, ritonavir, repaglinide, clarithromycin, and bosentan (Fig. 4). Overall Kpu ranged >200-fold between saquinavir and atorvastatin. The lack of correlation between Kpu and intracellular binding is of importance as several investigators (for example, Yamano et al., 1999, 2000) have assumed Kptotal (mainly driven by intracellular binding) to reflect an increase in cellular free concentration and hence the concentration available to enzymes (for metabolism or inhibition). In contrast, we have previously demonstrated similar inhibition potency in microsomes and hepatocytes after appropriate binding corrections for six inhibitors that showed a Kptotal range of 4 to 1200 (Brown et al., 2007).

The processes of active transport and cellular binding play distinct roles in defining Kptotal, which for all drugs in the current dataset exceeds 40 with a range >100-fold (Fig. 4). For several drugs, unbound intracellular concentrations are apparently equal to those in the plasma because of passive uptake, and Kpu values of unity are evident. In other cases, Kpu is substantially greater than one, as illustrated here for 16 substrates for hepatic transporters. Unlike Kptotal, Kpu requires determination of several parameters to describe both active and passive processes adequately. The current study emphasizes the need to understand the determinants of intracellular drug concentrations, to establish a more appropriate term than plasma concentration for in vitro-in vivo extrapolation and hence progress our mechanistic understanding of the rate determining processes contributing to drug clearance and governing drug-drug interactions.

Authorship Contributions

Participated in research design: Yabe, Galetin, and Houston.

Conducted experiments: Yabe.

Performed data analysis: Yabe.

Wrote or contributed to the writing of the manuscript: Yabe, Galetin, and Houston.

Footnotes

This work was partially funded by a consortium of pharmaceutical companies (GlaxoSmithKline, Lilly, Novartis, Pfizer, and Servier) within the Centre for Applied Pharmacokinetic Research at the University of Manchester and Daiichi-Sankyo Ltd.